Tibeuleu/FOC_Reduction
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

clean up add analysis step

Browse Source
This commit is contained in:
Thibault Barnouin
2022-05-24 09:01:39 +02:00
parent c8f6a47d15
commit a143850d80
3 changed files with 49 additions and 104 deletions
Download Patch File Download Diff File Expand all files Collapse all files

117
src/FOC_reduction.py
View File

@@ -20,17 +20,17 @@ from astropy.wcs import WCS
def main():
##### User inputs
## Input and output locations
# globals()['data_folder'] = "../data/NGC1068_x274020/"
# infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
# 'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
# 'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
# psf_file = 'NGC1068_f253m00.fits'
# globals()['plots_folder'] = "../plots/NGC1068_x274020/"
globals()['data_folder'] = "../data/NGC1068_x274020/"
infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
psf_file = 'NGC1068_f253m00.fits'
globals()['plots_folder'] = "../plots/NGC1068_x274020/"
globals()['data_folder'] = "../data/IC5063_x3nl030/"
infiles = ['x3nl0301r_c0f.fits','x3nl0302r_c0f.fits','x3nl0303r_c0f.fits']
psf_file = 'IC5063_f502m00.fits'
globals()['plots_folder'] = "../plots/IC5063_x3nl030/"
# globals()['data_folder'] = "../data/IC5063_x3nl030/"
# infiles = ['x3nl0301r_c0f.fits','x3nl0302r_c0f.fits','x3nl0303r_c0f.fits']
# psf_file = 'IC5063_f502m00.fits'
# globals()['plots_folder'] = "../plots/IC5063_x3nl030/"
# globals()['data_folder'] = "../data/NGC1068_x14w010/"
# infiles = ['x14w0101t_c0f.fits','x14w0102t_c0f.fits','x14w0103t_c0f.fits',
@@ -113,7 +113,7 @@ def main():
display_data = False
# Smoothing
smoothing_function = 'combine' #gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
smoothing_FWHM = 0.20 #If None, no smoothing is done
smoothing_FWHM = 0.10 #If None, no smoothing is done
smoothing_scale = 'arcsec' #pixel or arcsec
# Rotation
rotate_stokes = True #rotation to North convention can give erroneous results
@@ -122,7 +122,7 @@ def main():
crop = False #Crop to desired ROI
final_display = False
# Polarization map output
figname = 'IC5063_FOC' #target/intrument name
figname = 'NGC1068_FOC' #target/intrument name
figtype = '_combine_FWHM020' #additionnal informations
SNRp_cut = 5. #P measurments with SNR>3
SNRi_cut = 50. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
@@ -133,55 +133,40 @@ def main():
## Step 1:
# Get data from fits files and translate to flux in erg/cm²/s/Angstrom.
data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True)
# Crop data to remove outside blank margins.
data_array, error_array, headers = proj_red.crop_array(data_array, headers, step=5, null_val=0., inside=True, display=display_crop, savename=figname, plots_folder=plots_folder)
# Deconvolve data using Richardson-Lucy iterative algorithm with a gaussian PSF of given FWHM.
if deconvolve:
data_array = proj_red.deconvolve_array(data_array, headers, psf=psf, FWHM=psf_FWHM, scale=psf_scale, shape=psf_shape, iterations=iterations, algo=algo)
Dxy = np.ones(2)*10
data_mask = np.ones(data_array.shape[1:]).astype(bool)
# Align and rescale images with oversampling.
if px_scale.lower() not in ['full','integrate']:
data_array, error_array, headers, data_mask = proj_red.align_data(data_array, headers, error_array=error_array, upsample_factor=int(Dxy.min()), ref_center=align_center, return_shifts=False)
im = plt.imshow(error_array[0]/data_array[0]*100, origin='lower', vmin=0, vmax=100)
plt.colorbar(im)
wcs = WCS(headers[0])
plt.plot(*wcs.wcs.crpix,'r+')
plt.title("Align error")
plt.show()
# Rotate data to have North up
ref_header = deepcopy(headers[0])
if rotate_data:
alpha = ref_header['orientat']
data_mask = np.ones(data_array.shape[1:]).astype(bool)
alpha = headers[0]['orientat']
mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)], [np.sin(-alpha), np.cos(-alpha)]])
data_array, error_array, headers, data_mask = proj_red.rotate_data(data_array, error_array, data_mask, headers, -ref_header['orientat'])
im = plt.imshow(error_array[0]/data_array[0]*100, origin='lower', vmin=0, vmax=100)
plt.colorbar(im)
wcs = WCS(headers[0])
plt.plot(*wcs.wcs.crpix,'r+')
plt.title("Rotate error")
plt.show()
data_array, error_array, headers, data_mask = proj_red.rotate_data(data_array, error_array, data_mask, headers, -alpha)
# Align and rescale images with oversampling.
data_array, error_array, headers, data_mask = proj_red.align_data(data_array, headers, error_array=error_array, upsample_factor=10, ref_center=align_center, return_shifts=False)
# Rebin data to desired pixel size.
if rebin:
if px_scale.lower() in ['full','integrate']:
data_array, error_array, headers = proj_red.get_error(data_array, headers, error_array, data_mask, sub_shape=error_sub_shape, display=display_error, savename=figname+"_errors", plots_folder=plots_folder)
data_array, error_array, headers, Dxy, data_mask = proj_red.rebin_array(data_array, error_array, headers, pxsize=pxsize, scale=px_scale, operation=rebin_operation, data_mask=data_mask)
# Estimate error from data background, estimated from sub-image of desired sub_shape.
data_array, error_array, headers = proj_red.get_error(data_array, headers, error_array, data_mask, sub_shape=error_sub_shape, display=display_error, savename=figname+"_errors", plots_folder=plots_folder)
im = plt.imshow(error_array[0]/data_array[0]*100, origin='lower', vmin=0, vmax=100)
plt.colorbar(im)
wcs = WCS(headers[0])
plt.plot(*wcs.wcs.crpix,'r+')
plt.title("Background error")
plt.show()
# Estimate error from data background, estimated from sub-image of desired sub_shape.
if px_scale.lower() not in ['full','integrate']:
vertex = image_hull(data_mask,step=5,null_val=0.,inside=True)
else:
vertex = np.array([0.,0.,data_array.shape[2],data_array.shape[2]])
shape = np.array([vertex[1]-vertex[0],vertex[3]-vertex[2]])
rectangle = [vertex[2], vertex[0], shape[1], shape[0], 0., 'g']
data_array, error_array, headers = proj_red.get_error(data_array, headers, error_array, data_mask, sub_shape=error_sub_shape, display=display_error, savename=figname+"_errors", plots_folder=plots_folder)
#Plot array for checking output
if display_data:
if display_data and px_scale.lower() not in ['full','integrate']:
vertex = image_hull(data_mask,step=5,null_val=0.,inside=True)
shape = np.array([vertex[1]-vertex[0],vertex[3]-vertex[2]])
rectangle = [vertex[2], vertex[0], shape[1], shape[0], 0., 'g']
proj_plots.plot_obs(data_array, headers, vmin=data_array.min(), vmax=data_array.max(), rectangle =[rectangle,]*data_array.shape[0], savename=figname+"_center_"+align_center, plots_folder=plots_folder)
## Step 2:
@@ -192,29 +177,11 @@ def main():
# Bibcode : 1995chst.conf...10J
I_stokes, Q_stokes, U_stokes, Stokes_cov = proj_red.compute_Stokes(data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function)
im = plt.imshow(np.sqrt(Stokes_cov[0,0])/I_stokes*100, origin='lower', vmin=0, vmax=100)
plt.colorbar(im)
wcs = WCS(headers[0])
plt.plot(*wcs.wcs.crpix,'r+')
plt.title("Stokes error")
plt.show()
## Step 3:
# Rotate images to have North up
ref_header = deepcopy(headers[0])
if rotate_stokes:
alpha = ref_header['orientat']
mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)],
[np.sin(-alpha), np.cos(-alpha)]])
rectangle[0:2] = np.dot(mrot, np.asarray(rectangle[0:2]))+np.array(data_array.shape[1:])/2
rectangle[4] = alpha
I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, data_mask = proj_red.rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, -ref_header['orientat'], SNRi_cut=None)
im = plt.imshow(np.sqrt(Stokes_cov[0,0])/I_stokes*100, origin='lower', vmin=0, vmax=100)
plt.colorbar(im)
wcs = WCS(headers[0])
plt.plot(*wcs.wcs.crpix,'r+')
plt.title("Rotate Stokes error")
plt.show()
alpha = headers[0]['orientat']
I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, data_mask = proj_red.rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, -alpha, SNRi_cut=None)
# Compute polarimetric parameters (polarization degree and angle).
P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P = proj_red.compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers)
@@ -235,15 +202,17 @@ def main():
# Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error).
if px_scale.lower() not in ['full','integrate'] and final_display:
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display=None)
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_flux", plots_folder=plots_folder, display='Pol_Flux')
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P", plots_folder=plots_folder, display='Pol_deg')
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_I_err", plots_folder=plots_folder, display='I_err')
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_err", plots_folder=plots_folder, display='Pol_deg_err')
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRi", plots_folder=plots_folder, display='SNRi')
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRp", plots_folder=plots_folder, display='SNRp')
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display=None)
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_flux", plots_folder=plots_folder, display='Pol_Flux')
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P", plots_folder=plots_folder, display='Pol_deg')
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_I_err", plots_folder=plots_folder, display='I_err')
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_err", plots_folder=plots_folder, display='Pol_deg_err')
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRi", plots_folder=plots_folder, display='SNRi')
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRp", plots_folder=plots_folder, display='SNRp')
elif final_display:
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display='default')
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display='default')
elif px_scale.lower() not in ['full', 'integrate']:
pol_map = proj_plots.pol_map(Stokes_test, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut)
return 0